Model Quantization
Quantization is the process of reducing an AI model's numerical precision (e.g., from 16-bit to 4-bit) to dramatically shrink its memory footprint and speed up inference, making large models runnable on consumer hardware.
How it works & Why it matters
A 70-billion parameter model at full precision needs 140GB of memory, far beyond any consumer GPU. Quantized to 4-bit (Q4_K_M), the same model fits in 40GB and runs on a Mac Studio. The quality loss is minimal for most business tasks: benchmarks show 4-bit quantized models retain 95-98% of the original model's capability. For businesses running on-premise AI, quantization is what makes the economics work. Tools like llama.cpp, GGUF format, and Ollama handle quantization automatically, so no ML expertise is required.
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